Las Vegas algorithms for gene recognition: Suboptimal and error-tolerant spliced alignment

Conference · Mon Dec 01 04:00:00 EST 1997

OSTI ID:549029

Sze, Sing-Hoi; Pevzner, P A ^[1]

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1. Univ. of Southern California, Los Angeles, CA (United States)

Recently, Gelfand, Mironov and Pevzner proposed a spliced alignment approach to gene recognition that provides 99% accurate recognition of human gene if a related mammalian protein is available. However, even 99% accurate gene predictions are insufficient for automated sequence annotation in large-scale sequencing projects and therefore have to be complemented by experimental gene verification. 100% accurate gene predictions would lead to a substantial reduction of experimental work on gene identification. Our goal is to develop an algorithm that either predicts an exon assembly with accuracy sufficient for sequence annotation or warns a biologist that the accuracy of a prediction is insufficient and further experimental work is required. We study suboptimal and error-tolerant spliced alignment problems as the first steps towards such an algorithm, and report an algorithm which provides 100% accurate recognition of human genes in 37% of cases (if a related mammalian protein is available). For 52% of genes, the algorithm predicts at least one exon with 100% accuracy. 30 refs., 1 fig., 3 tabs.

• Research Organization: Association for Computing Machinery, New York, NY (United States); Sloan (Alfred P.) Foundation, New York, NY (United States)
• DOE Contract Number: FG02-94ER61919
• OSTI ID: 549029
• Report Number(s): CONF-970137--
• Country of Publication: United States
• Language: English

Related Subjects

55 BIOLOGY AND MEDICINE
BASIC STUDIES
99 GENERAL AND MISCELLANEOUS
ACCURACY
ALGORITHMS
AMINO ACID SEQUENCE
AUTOMATION
CORRELATIONS
DATA
DNA SEQUENCING
ERRORS
EXONS
GENES
GENETIC MAPPING
MAMMALS
MATHEMATICAL MODELS
MOLECULAR BIOLOGY
NUCLEOTIDES
PROTEINS
SPLICING
VERIFICATION